Qwen3Fangwusha4B

Model Card for Qwen3Fangwusha4B

Qwen3Fangwusha4B is a 4B-parameter Chinese large language model fine-tuned based on Qwen3, optimized for Chinese text understanding, generation, information extraction, and practical task-oriented dialogue and content processing.

Model Details

Model Description

This model is a fine-tuned version of the Qwen3-4B base model, focusing on improving Chinese language understanding and generation capabilities in real-world application scenarios. It is designed for efficient deployment while maintaining strong performance in common NLP tasks.

• Developed by: Yougen Yuan
• Funded by [optional]: Personal Research Project
• Shared by [optional]: Yougen Yuan
• Model type: Decoder-only Large Language Model
• Language(s) (NLP): Chinese (Simplified)
• License: Apache-2.0
• Finetuned from model [optional]: Qwen3-4B

Model Sources [optional]

• Repository: https://huggingface.co/Yougen/Qwen3Fangwusha4B
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be directly used for:

• Chinese text generation and rewriting
• Text completion and continuation
• Short dialogue and question answering
• Information extraction and text summarization
• Lightweight content creation and auxiliary writing

Downstream Use [optional]

Can be further fine-tuned for:

• Domain-specific question answering systems
• Customer service chatbots
• RAG (Retrieval-Augmented Generation) systems
• Text classification and sentiment analysis
• Professional document processing

Out-of-Scope Use

• Not designed for high-risk decision-making scenarios such as medical diagnosis, financial investment, or legal advice without professional review
• Not suitable for generating harmful, misleading, illegal, or privacy-violating content
• Not optimized for non-Chinese languages
• Not intended for large-scale long-context reasoning beyond its design limits

Bias, Risks, and Limitations

• The model may inherit social, cultural, and linguistic biases present in the pre-training data of the original Qwen3 model.
• It may produce inaccurate or inconsistent responses to complex professional or sensitive topics.
• Output quality may degrade for extremely colloquial, fragmented, or noisy text inputs.
• The model does not have autonomous fact-checking capabilities and may generate factually incorrect content.

Recommendations

Users should verify key outputs, especially in professional or production environments. Sensitive information and high-stakes scenarios require human supervision. It is recommended to add content filtering mechanisms for public deployment. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

How to Get Started with the Model

Use the code below to load and inference with the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Yougen/Qwen3Fangwusha4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "请解释一下大模型微调的基本流程"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

Training data includes high-quality Chinese text corpora covering general knowledge, web documents, instructional data, and task-specific fine-tuning pairs. Data has been processed with deduplication, noise removal, and quality filtering.

Training Procedure

Preprocessing [optional]

• Text cleaning and special character filtering
• Instruction tuning format construction
• Fixed-length tokenization with truncation and padding

Training Hyperparameters

• Training regime: bf16 mixed precision
• Learning rate: 2e-5
• Batch size: 16
• Optimizer: AdamW
• Weight decay: 0.01
• Epochs: 3

Speeds, Sizes, Times [optional]

• Model size: 4B parameters
• Training hardware: NVIDIA A100 / RTX 4090 series GPU
• Training time: Multiple hours

Evaluation

Testing Data, Factors & Metrics

Testing Data

Internal Chinese evaluation set including general knowledge questions, text generation, and understanding benchmarks.

Factors

Text length, domain complexity, instruction following ability, generation fluency.

Metrics

• Perplexity
• BLEU
• Human evaluation of fluency and accuracy
• Instruction following success rate

Results

[More Information Needed]

Summary

The model shows stable and improved performance in Chinese understanding and generation tasks, with good inference efficiency for middle-scale applications.

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA GPU
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Decoder-only transformer architecture based on Qwen3. Optimized for Chinese instruction following, text generation, and practical task completion.

Compute Infrastructure

Hardware

NVIDIA GPU with CUDA support

Software

• PyTorch
• Hugging Face Transformers
• Accelerate
• Datasets
• Tokenizers

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

• Decoder-only: A transformer architecture that uses only decoder layers for autoregressive generation.
• Fine-tuning: The process of adapting a pre-trained model to specific downstream tasks.
• Qwen3: A series of large language models developed by Alibaba Cloud Tongyi Laboratory.

More Information [optional]

For updates and issues, please visit the model repository on Hugging Face Hub.

Model Card Authors [optional]

Yougen Yuan

Model Card Contact

[More Information Needed]